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Utilization of machine learning and neural networks to optimize the enclosure angle, magnetic field, and radiation parameter for mixed convection of hybrid nanofluid flow next to assess environmental impact

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engineering Analysis with Boundary Elements 2023年 146卷 252-262页

作者： Tao Hai Hayder A. Dhahad Masood Ashraf Ali Vishal Goyal Sattam Fahad Almojil Abdulaziz Ibrahim Almohana Abdulrhman Fahmi Alali Khaled Twfiq Almoalimi Farah Qasim Ahmed Alyousuf School of Computer and Information Qiannan Normal University for Nationalities Duyun Guizhou 558000 China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Duyun Guizhou 558000 China Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Shah Alam Selangor 40450 Malaysia Mechanical Engineering Department University of Technology Baghdad Iraq Department of Industrial Engineering College of Engineering Prince Sattam bin Abdulaziz University Alkharj 16273 Saudi Arabia Department of Electronics and Communication Engineering GLA University Mathura India Department of Civil Engineering College of Engineering King Saud University P.O. Box 800 Riyadh 11421 Saudi Arabia Department of Information Technology College of Engineering and Computer Science Lebanese French University Kurdistan Region Iraq

This paper examines the mixed convective heat transfer (HTR) of nanofluid (NFD) flow in a rectangular enclosure with the upper moving wall numerically. The lower wall has a high temperature and a number of semi-circular obstacles with the same temperature are installed on it. The upper moving wall has a low temperature and the other two walls are insulated. The enclosure can change from horizontal to vertical. Radiation HTR is considered in the enclosure and there is a magnetic field (MGF) that can change the angle from horizontal to vertical affecting the NFD. This study is carried out for different angles of the enclosure and MGF from horizontal to vertical for radiation parameters (RDP) of 0 to 3 and a constant MGF with Hartmann number of 20 and Richardson number of 10. The aim is to estimate the Nusselt number (Nu), entropy generation (ETG), and Bejan number (Be). The SIMPLE algorithm is utilized using FORTRAN software, and optimization is done using artificial intelligence to find the maximum and minimum output values. The results demonstrate that the maximum value of Nu and Bes corresponds to the MGF angle and enclosure angle of 90°. The minimum value of the Nu and the maximum amount of ETG corresponds to the horizontal MGF and horizontal enclosure when the RDP is 1.5. An increment in the RDP enhances the amount of Nu. The maximum amount of ETG, i.e. 12.87, corresponds to the enclosure with an angle of 45° for the horizontal MGF and the absence of RDP. corresponds to the enclosure with an angle of 45° for the horizontal MGF and the absence of RDP. It was also found that most environmental impacts, and hence values for different environmental factors, arise from the production of nanoparticles; thus, it is a significant contributor to environmental impacts.

关键词： Machine learning Entropy generation Numerical calculations Convection Nanofluid

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Recent advances of CDs-based ternary heterojunctions for enhancing photocatalytic performance in the degradation of organic pollutants: A review

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Nano-Structures and Nano-Objects 2024年 37卷

作者： Zulfa, Liyana Labiba Oktavianti, Nanik Ismi Hidayat, Alvin Romadhoni Putra Utomo, Wahyu Prasetyo Putri, Desindy Ramadhani Hartanto, Djoko Widyastuti Ediati, Ratna Department of Chemistry Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Jl. Arif Rahman Hakim Kampus ITS Keputih-Sukolilo Surabaya 60111 Indonesia School of Energy and Environment City University of Hong Kong Hong Kong 999077 China Department of Materials and Metallurgical Engineering Faculty of Industrial Technology and Systems Engineering Institut Teknologi Sepuluh Nopember Jl. Arif Rahman Hakim Kampus ITS Keputih-Sukolilo Surabaya 60111 Indonesia

Wastewater containing organic pollutants, such as dyes, antibiotics or pesticides, is a major environmental problem. Photocatalysis is an effective, environmentally friendly and economically viable method for degrading hazardous organic pollutants in wastewater. Among the heterojunction systems, the ternary heterojunction system is the most effective system due to its higher electron transfer, excellent redox capacity and light-harvesting ability, which are advantageous in photocatalytic degradation of organic pollutants. Carbon dots (CDs) are often used as a component in ternary heterojunctions due to their strong absorption within the various regions of the solar spectrum, tunable band structure and up-conversion photoluminescence (UCPL) properties. The optical properties of CDs-based ternary heterojunctions enable faster migration of multiple carriers, effective utilization of sunlight and a shorter migration pathway which enhances the organic pollutant photodegradation efficiency. A profound understanding of the synergistic effects between CDs and semiconductors in ternary heterojunction systems as photocatalysts for organic pollutants degradation is essential for future development. In this review, we comprehensively discuss recent advances of CDs derived from various precursors and their utilization in ternary heterojunction photocatalysts. Furthermore, we conduct a critical analysis on the synergistic effect between CDs and semiconductors for fabricating photocatalysts with optimal organic pollutant degradation performance. In addition, we elaborate on the charge transfer reaction mechanisms and several factors that influence the performance of CDs-based ternary heterojunctions. We also present research gaps in the use of CDs-based heterojunctions for organic pollutant degradation and state future perspectives and research areas. This review provides an insightful discussion on the development of CDs-based ternary heterojunctions for organic pollutant degrada

关键词： Carbon dots Carbon dots-based ternary heterojunction Organic pollutants Photocatalytic Water treatment

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Author Correction: CRISPR screens reveal convergent targeting strategies against evolutionarily distinct chemoresistance in cancer

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Nature communications 2025年第1期16卷 1569页

作者： Chunge Zhong Wen-Jie Jiang Yingjia Yao Zexu Li You Li Shengnan Wang Xiaofeng Wang Wenjuan Zhu Siqi Wu Jing Wang Shuangshuang Fan Shixin Ma Yeshu Liu Han Zhang Wenchang Zhao Lu Zhao Yi Feng Zihan Li Ruifang Guo Li Yu Fengyun Pei Jun Hu Xingzhi Feng Zihuan Yang Zhengjia Yang Xueying Yang Yue Hou Danni Zhang Dake Xu Ren Sheng Yihao Li Lijun Liu Hua-Jun Wu Jun Huang Teng Fei Key Laboratory of Bioresource Research and Development of Liaoning Province College of Life and Health Sciences Northeastern University Shenyang 110819 China. National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang 110819 China. Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University) Ministry of Education Shenyang 110819 China. Foshan Graduate School of Innovation Northeastern University Foshan 528311 China. Peking University Third Hospital Beijing 100191 China. Department of Colorectal Surgery the Sixth Affiliated Hospital Sun Yat-sen University Guangzhou China. Clinical Research Center the Sixth Affiliated Hospital Sun Yat-sen University Guangzhou China. Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases the Sixth Affiliated Hospital Sun Yat-sen University Guangzhou China. Guangdong Institute of Gastroenterology Guangzhou China. Department of Cardiothoracic Surgery Jinqiu Hospital of Liaoning Province Shenyang China. Department of Thoracic Surgery The Fourth Affiliated Hospital of China Medical University Shenyang China. Key Laboratory for Anisotropy and Texture of Materials (Ministry of Education) School of Materials Science and Engineering Northeastern University Shenyang China. BeiGene Institute BeiGene (Shanghai) Research & Development Co. Ltd 200131 Shanghai China. Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing) Peking University Cancer Hospital & Institute Beijing 100142 China. hjwu@***. Department of Biomedical Informatics School of Basic Medical Sciences Peking University Health Science Center Beijing 100191 China. hjwu@***. Center for Precision Medicine Multi-Omics Research Institute of Advanced Clinical Medicine Peking University Beijing 100191 China. hjwu@***. Department of Colorectal Surgery the Sixth Affiliated Hospital Sun Yat-sen University Guangzhou China. huan

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Facile synthesis of Ni-ZIF-8 with improved photodegradation performance for methylene blue

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Case Studies in Chemical and Environmental engineering 2024年 10卷

作者： Zulfa, Liyana Labiba Hidayat, Alvin Romadhoni Putra Utomo, Wahyu Prasetyo Subagyo, Riki Kusumawati, Etty Nurlia Kusumawati, Yuly Hartanto, Djoko Widyastuti, Widyastuti Ediati, Ratna Department of Chemistry Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Jl. Arif Rahman Hakim Kampus ITS Keputih-Sukolilo Surabaya 60111 Indonesia School of Energy and Environment City University of Hong Kong Hong Kong 999077 China Chemistry Course Faculty of Science and Engineering Iwate University 4-3-5 Ueda Iwate Morioka 020-8551 Japan Department of Materials and Metallurgical Engineering Faculty of Industrial Technology and Systems Engineering Institut Teknologi Sepuluh Nopember Jl. Arif Rahman Hakim Kampus ITS Keputih-Sukolilo Surabaya 60111 Indonesia

A series of bimetallic metal-organic frameworks (MOFs) photocatalysts had been successfully synthesized by doping nickel (Ni) into zeolitic imidazolate framework-8 (ZIF-8) for enhanced photodegradation of methylene blue (MB). Optical characterization revealed that the Ni-ZIF-8 composites possessed a narrower bandgap and lower recombination rate of charge carriers than pristine ZIF-8. The Ni(20)-ZIF-8 composite showed the highest MB photodegradation efficiency of up to 93.22 % in 150 min, with the use of 30 mg photocatalyst, 50 mL of 30 mg/L MB solution, and a neutral pH. The scavenger test clearly proved that h⁺ and •OH were the main active species. © 2024 The Authors

关键词： Bimetallic ZIF MB degradation Ni doping Photocatalysis Wastewater treatment

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Enhanced Photocatalytic Degradation of Methylene Blue Using Bimetallic C,N-Doped NiO/ZnO Derived from Metal-Organic Frameworks

SSRN

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SSRN 2024年

作者： Zulfa, Liyana Labiba Hidayat, Alvin Romadhoni Putra Ali, Badrut Tamam Ibnu Utomo, Wahyu Prasetyo Kusumawati, Etty Nurlia Hartanto, Djoko Ediati, Ratna Widyastuti Department of Chemistry Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Jl. Arif Rahman Hakim Kampus ITS Keputih-Sukolilo Surabaya60111 Indonesia Research Center for Process and Manufacturing Industry Technology National Research and Innovation Agency Technology Energy Cluster PUSPIPTEK South Tangerang625 Building Indonesia School of Energy and Environment City University of Hong Kong 999077 Hong Kong Chemistry Course Faculty of Science and Engineering Iwate University 4-3-5 Ueda Iwate Morioka020-8551 Japan Department of Materials and Metallurgical Engineering Faculty of Industrial Technology and Systems Engineering Institut Teknologi Sepuluh Nopember Jl. Arif Rahman Hakim Kampus ITS Keputih-Sukolilo Surabaya60111 Indonesia

Organic waste pollution is a severe environmental and human health issue that needs to be addressed urgently. Photocatalysis has emerged as an effective method for removing contaminants from organic wastes. Porous metal oxides derived from metal-organic frameworks (MOFs) have been widely developed and applied to organic waste degradation processes. This study successfully synthesized p-n heterojunction NiO-ZnO doped with carbon and nitrogen using Ni-ZIF-8 as a template through a two-step calcination process. The inert atmosphere preserved the carbon and nitrogen contents present in Ni-ZIF-8, resulting in carbon- and nitrogen-doped NiO-ZnO. The presence of the p-n heterojunction system and carbon and nitrogen doping was confirmed by X-ray Diffraction (XRD), X-ray Photoelectron Spectroscopy (XPS), and optical characterization. Carbon and nitrogen-doped NiO-ZnO exhibit broad light absorption, low recombination rates, and low charge resistance, which are beneficial for methylene blue (MB) degradation. Specifically, C, N-NiO(20)-ZnO demonstrated the best MB degradation performance, achieving ~100% degradation within 150 min and maintaining high stability over four cycles. Scavenger tests indicated that •OH played a crucial role in MB degradation. This research provides new insights into the synergistic effect of the p-n heterojunction system and carbon and nitrogen doping to enhance the degradation performance of organic waste. © 2024, The Authors. All rights reserved.

关键词： Semiconductor doping

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A Brief Review of Explainable Artificial Intelligence in Healthcare

arXiv

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arXiv 2023年

作者： Sadeghi, Zahra Alizadehsani, Roohallah Cifci, Mehmet Akif Kausar, Samina Rehman, Rizwan Mahanta, Priyakshi Bora, Pranjal Kumar Almasri, Ammar Alkhawaldeh, Rami S. Hussain, Sadiq Alatas, Bilal Shoeibi, Afshin Moosaei, Hossein Hladík, Milan Nahavandi, Saeid Pardalos, Panos M. Institute for Big Data Analytics Faculty of Computer Science Dalhousie University Canada Deakin University Geelong Australia The Institute of Computer Technology Tu Wien University Vienna1040 Austria University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Centre for Computer Science and Applications Dibrugarh University Assam India Department of Management Information Sys Al-Balqa Applied University Salt19117 Jordan Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Examination Branch Dibrugarh University Assam Dibrugarh India Department of Software Eng. Firat University Elazig23100 Turkey Data Science and Computational Intelligence Institute University of Granada Spain Department of Informatics Faculty of Science Jan Evangelista Purkyně University in Ústí nad Labem Czech Republic Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville32611 United States

XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such as medical domain have increased the demand for transparency and explainability since wrong predictions may have severe consequences. Model explainability and interpretability are vital successful deployment of AI models in healthcare practices. AI applications' underlying reasoning needs to be transparent to clinicians in order to gain their trust. This paper presents a systematic review of XAI aspects and challenges in the healthcare domain. The primary goals of this study are to review various XAI methods, their challenges, and related machine learning models in healthcare. The methods are discussed under six categories: Features-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric methods. Most importantly, the paper explores XAI role in healthcare problems to clarify its necessity in safety-critical applications. The paper intends to establish a comprehensive understanding of XAI-related applications in the healthcare field by reviewing the related experimental results. To facilitate future research for filling research gaps, the importance of XAI models from different viewpoints and their limitations are investigated. © 2023, CC BY-NC-ND.

关键词： Decision making

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Predicting the risk of ischemic stroke in patients with atrial fibrillation using heterogeneous drug-protein-disease network-based deep learning

arXiv

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arXiv 2024年

作者： Lyu, Zhiheng Yang, Jiannan Xu, Zhongzhi Wang, Weilan Cheng, Weibin Tsui, Kwok-Leung Tse, Gary Zhang, Qingpeng School of Data Science City University of Hong Kong Hong Kong Musketeers Foundation Institute of Data Science The University of Hong Kong Hong Kong School of Public Health Sun Yat-sen University Guangzhou China Centre for Healthy Longevity Yong Loo Lin School of Medicine National University of Singapore Singapore Institute for Healthcare Artificial Intelligence Guangdong Second Provincial General Hospital Guangzhou China Grado Department of Industrial and Systems Engineering Virginia Polytechnic Institute State University United States Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease Department of Cardiology Tianjin Institute of Cardiology Second Hospital of Tianjin Medical University Tianjin China Cardiac Electrophysiology Unit Cardiovascular Analytics Group Kowloon Hong Kong School of Nursing and Health Studies Hong Kongetropolitan University Homantin Hong Kong Department of Pharmacology and Pharmacy LKS Faculty of Medicine The University of Hong Kong Hong Kong

Existing risk assessment models for predicting ischemic stroke (IS) in atrial fibrillation (AF) patients overlook the impacts of medications and the intricacy of drug-protein-disease interplays. We aim to develop an interpretable deep learning model, namely the AF-Biological-IS-Path (ABioSPATH), to predict the one-year IS risk in AF patients, and elucidate the underlying molecular mechanisms by integrating drug-protein-disease pathways and real-world clinical data of AF patients. A heterogeneous multilayer drug-protein-disease network was constructed to identify the mechanism of drug action and disease comorbidity propagation pathways. By integrating mechanism pathways and patient characteristics, ABioSPATH generated the IS risk and potential pathways for each patient. The Electronic Health Record (EHR) data of 7859 patients diagnosed with AF between 01/2008-12/2009 from 43 hospitals in Hong Kong were included. ABioSPATH outperformed all baselines across all metrics (AUROC=0.7815;95% CI: 0.7346-0.8283, PPV=0.430, NPV=0.870, sensitivity=0.500, specificity=0.885, average precision=0.409, Brier score=0.195). In the cohort-level analysis, key proteins in the ten most common pathways, including CRP, REN, and PTGS2, were identified. A subsequent individual level analysis revealed the PIK3/Akt and cytokine and chemokine-induced signaling pathways among the top twenty important ones. Potential IS risks tied to less-studied drugs like prochlorperazine maleate were also highlighted. ABioSPATH demonstrated superior predictive performance in estimating IS risk with valuable molecular-level insights for clinical use. The advantage is that only routinely collected data are required, without requiring expensive biomarkers to be tested. Its applications extend beyond IS, indicating potential for risk screening in other diseases, enhancing patient care, and offering insights for drug development. Copyright © 2024, The Authors. All rights reserved.

关键词： Risk assessment

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A Brief Review of Explainable Artificial Intelligence in Healthcare

SSRN

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SSRN 2023年

作者： Sadeghi, Zahra Alizadehsani, Roohallah Cifci, Mehmet Akif Kausar, Samina Rehman, Rizwan Mahanta, Priyakshi Bora, Pranjal Kumar Almasri, Ammar Alkhawaldeh, Rami S. Hussain, Sadiq Alatas, Bilal Shoeibi, Afshin Moosaei, Hossein Hladík, Milan Nahavandi, Saeid Pardalo, Panos M. Institute for Big Data Analytics Faculty of Computer Science Dalhousie University Canada Deakin University Geelong Australia The Institute of Computer Technology Tu Wien University Vienna1040 Austria University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Centre for Computer Science and Applications Dibrugarh University Assam India Department of Management Information Sys Al-Balqa Applied University Salt19117 Jordan Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Examination Branch Dibrugarh University Assam Dibrugarh India Department of Software Eng. Firat University Elazig23100 Turkey Data Science and Computational Intelligence Institute University of Granada Spain Department of Informatics Faculty of Science Jan Evangelista Purkyně University in Ústí nad Labem Czech Republic Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Swinburne University of Technology HawthornVIC3122 Australia Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville32611 United States

关键词： Decision making

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Operational Research: Methods and Applications

arXiv

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arXiv 2023年

作者： Petropoulos, Fotios Laporte, Gilbert Aktas, Emel Alumur, Sibel A. Archetti, Claudia Ayhan, Hayriye Battarra, Maria Bennell, Julia A. Bourjolly, Jean-Marie Boylan, John E. Breton, Michèle Canca, David Charlin, Laurent Chen, Bo Cicek, Cihan Tugrul Cox, Louis Anthony Currie, Christine S.M. Demeulemeester, Erik Ding, Li Disney, Stephen M. Ehrgott, Matthias Eppler, Martin J. Erdogan, Güneş Fortz, Bernard Franco, L. Alberto Frische, Jens Greco, Salvatore Gregory, Amanda J. Hämäläinen, Raimo P. Herroelen, Willy Hewitt, Mike Holmström, Jan Hooker, John N. Işık, Tugçe Johnes, Jill Kara, Bahar Y. Karsu, Özlem Kent, Katherine Köhler, Charlotte Kunc, Martin Kuo, Yong-Hong Letchford, Adam N. Leung, Janny Li, Dong Li, Haitao Lienert, Judit Ljubić, Ivana Lodi, Andrea Lozano, Sebastián Lurkin, Virginie Martello, Silvano McHale, Ian G. Midgley, Gerald Morecroft, John D.W. Mutha, Akshay Oguz, Ceyda Petrovic, Sanja Pferschy, Ulrich Psaraftis, Harilaos N. Rose, Sam Saarinen, Lauri Salhi, Said Song, Jing-Sheng Sotiros, Dimitrios Stecke, Kathryn E. Strauss, Arne K. Tarhan, Istenç Thielen, Clemens Toth, Paolo Van Woensel, Tom Vanden Berghe, Greet Vasilakis, Christos Vaze, Vikrant Vigo, Daniele Virtanen, Kai Wang, Xun Weron, Rafal White, Leroy Yearworth, Mike Yıldırım, E. Alper Zaccour, Georges Zhao, Xuying School of Management University of Bath Bath United Kingdom Makridakis Open Forecasting Center University of Nicosia Nicosia Cyprus Department of Decision Sciences HEC Montréal Montreal Canada Molde University College Molde Norway Cranfield School of Management Cranfield University Cranfield United Kingdom Department of Management Sciences University of Waterloo Waterloo Canada ESSEC Business School in Paris Department of Information Systems Decision Sciences and Statistics Cergy France H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta United States Centre for Decision Research Leeds University Business School University of Leeds Leeds United Kingdom Université du Québec à Montréal Montreal Canada Centre for Marketing Analytics and Forecasting Lancaster University Management School Lancaster University Lancaster United Kingdom School of Engineering Department of Industrial Engineering and Management Science I University of Seville Seville Spain Mila-Quebec AI Institute Montreal Canada Warwick Business School University of Warwick Coventry United Kingdom Department of Industrial Engineering Atilim University Ankara Turkey Department of Business Analytics University of Colorado School of Business Denver United States Cox Associates Denver United States Mathematical Sciences University of Southampton Southampton United Kingdom Faculty of Economics and Business Research Center for Operations Management KU Leuven Leuven Belgium Durham University Business School Durham University Durham United Kingdom Center for Simulation Analytics and Modelling University of Exeter Business School Exeter United Kingdom Department of Management Science Lancaster University Management School Lancaster University Lancaster United Kingdom University of St. Gallen St. Gallen Switzerland HEC - Management School of the University of Liège Liège Belgium Département d'Informatique Université Libre de Bruxelles Bruss

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes. © 2023, CC BY.

关键词： Decision making

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Intention-sensitive Preference Learning Network for Personalized Session-based Recommendation

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ACM Transactions on Recommender systems 1000年

作者： Qingbo Zhang Xiaochun Yang Hao Chen Bin Wang Xiangmin Zhou Computer Science and Engineering Northeastern University Shenyang China Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang China Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Ministry of Education Shenyang China School of Computing Technologies RMIT University Melbourne Australia

Nowadays, research on session-based recommender systems (SRSs) is one of the hot spots in the recommendation domain. Existing methods make recommendations based on the user’s current intention (also called short-term preference) during a session, often overlooking the specific preferences associated with these intentions. In reality, users usually exhibit diverse preferences for different intentions, and even for the same intention, individual preferences can vary significantly between users. As users interact with items throughout a session, their intentions can shift accordingly. To enhance recommendation quality, it is crucial not only to consider the user’s intentions but also to dynamically learn their varying preferences as these intentions change. In this paper, we propose a novel Intention-sensitive Preference Learning Network (IPLN) including three main modules: intention recognizer, preference detector, and prediction layer. Specifically, the intention recognizer infers the user’s underlying intention within his/her current session by analyzing complex relationships among items. Based on the acquired intention, the preference detector learns the intention-specific preference by selectively integrating latent features from items in the user’s historical sessions. Besides, the user’s general preference is utilized to refine the obtained preference to reduce the potential noise carried from historical records. Ultimately, the fine-tuned preference and intention collaborate to instruct the next-item recommendation in the prediction layer. To prove the effectiveness of the proposed IPLN, we perform extensive experiments on two real-world datasets. The experiment results demonstrate the superiority of IPLN compared with other state-of-the-art models.

关键词： session-based recommender systems personalized next-item recommendation user intention intention-specific preference

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